GPS-based Tracking over WSNs with Delayed and Missing Data using UFIR Filtering

Abstract

Vehicles tracking is organized to increase safety in smart cities by localizing cars using the Global Positioning System (GPS). The GPS-based system provides accurate tracking, but is also required to be reliable and robust. As a main estimator, we propose using the unbiased finite impulse response (UFIR) filter, which meets these needs as a more robust alternative to the Kalman filter (KF). The UFIR filter is developed for vehicle tracking in discrete-time state-space over wireless sensor networks (WSNs) with time-stamped discretely delayed on k-step-lags and missing data. The state-space model is represented in a way such that the UFIR filter, KF, and H∞ filter can be used universally. Applications are given for measurement data, which are cooperatively transferred from a vehicle to a central station through several nodes with k-step-lags. Better tracking performance of the UFIR filter is shown experimentally.